class_docstr: Class handler for using a ModelAgnosticEvaluation graph.
docstr: Creates a graph which we instantiate FPL infeed and metric ops.
docstr_tok: ["Creates","a","graph","which","we","instantiate","FPL","infeed","and","metric","ops","."]
code: def _construct_graph(self):
      """Creates a graph which we instantiate FPL infeed and metric ops."""
      with self._graph.as_default():
          self.input_serialized_example = tf.compat.v1.placeholder(
              dtype=tf.string
          )
          features = tf.io.parse_example(
              serialized=self.input_serialized_example,
              features=self._config.feature_spec,
          )
          self._features_map = features
          for label in self._config.label_keys:
              self._labels_map[label] = self._features_map[label]
          for pred in self._config.prediction_keys:
              self._predictions_map[pred] = self._features_map[pred]
          self.register_add_metric_callbacks(
              self._add_metrics_callbacks
          )
          self._perform_metrics_update_fn = self._session.make_callable(
              fetches=self._all_metric_update_ops,
              feed_list=[self.input_serialized_example],
          )
  
file_path: model-analysis/tensorflow_model_analysis/model_agnostic_eval/model_agnostic_evaluate_graph.py
imports: from typing import List, Optional
  import tensorflow
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_metrics_graph import eval_metrics_graph
  from tensorflow_model_analysis.model_agnostic_eval import model_agnostic_predict
label_desc: Use image node_id 2 for calling the ModelAgnosticEvaluateGraph obj's underlying member method code with example usage: obj._construct_graph() without return types